code: def test_generate_data(self):
      base_env_name = "CartPole-v0"
      batch_size = 5
      reward_range = (-1, 1)
      nsteps = 100
      ep, num_dones, num_dones_in_last_step = self.play_env(
          base_env_name=base_env_name,
          batch_size=batch_size,
          reward_range=reward_range,
          nsteps=nsteps,
      )
  
      # This is because every num_dones starts a new trajectory, and a further
      # batch_size are active at the last step when we call generate_data, but
      # the ones that got done in the last step (these have only one time-step in
      # their trajectory) will be skipped.
      expected_num_trajectories = (
          num_dones + batch_size - num_dones_in_last_step
      )
  
      # Similar logic as above, nsteps * batch_size overall `step` calls are made.
      expected_num_time_steps = (
          (nsteps * batch_size)
          + num_dones
          + batch_size
          - num_dones_in_last_step
      )
  
      # Dump the completed data to disk.
      ep.generate_data(self.tmp_dir, self.tmp_dir)
  
      # Read the written files and assert on the number of time steps.
      training_filenames = ep.training_filepaths(
          self.tmp_dir, ep.num_shards[problem.DatasetSplit.TRAIN], True
      )
      dev_filenames = ep.dev_filepaths(
          self.tmp_dir, ep.num_shards[problem.DatasetSplit.EVAL], True
      )
  
      (
          training_trajectories,
          training_timesteps,
      ) = self.read_tfrecord_dataset(training_filenames, ep)
      dev_trajectories, dev_timesteps = self.read_tfrecord_dataset(
          dev_filenames, ep
      )
  
      # This tests what we wrote on disk matches with what we computed.
      self.assertEqual(
          expected_num_time_steps, training_timesteps + dev_timesteps
      )
      self.assertEqual(
          expected_num_trajectories,
          training_trajectories + dev_trajectories,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import gym
  from gym.spaces import Box
  from gym.spaces import Discrete
  import numpy
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.envs import env_problem
  from tensor2tensor.envs import env_problem_utils
  from tensor2tensor.envs import gym_env_problem
  from tensor2tensor.layers import modalities
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 9 for calling the GymEnvProblemTest obj's underlying member method code with example usage: obj.test_generate_data() without return types